Role of Computational Science in Materials and Systems Design for Sustainable Energy Applications: An Industry Perspective

Maheshwari, Shantanu; Shetty, Sharankumar; Ratnakar, Ram R.; Sanyal, Sankar P.

doi:10.1007/s41745-021-00275-9

Cited by 4 publications

(1 citation statement)

References 123 publications

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“…Aaryashree, Sahoo, et al [8] In this study, investigate the recent developments and potential benefits of smart chemical sensors that are both wearable and self-powered.…”

Section: Introductionmentioning

confidence: 99%

Utilization of Renewable energy for Industrial Applications using Quantum Computing

Rajawat

Pant

Goyal

2022

Glo.Jou.Nov.Res.App.Sci

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Even though the energy and utilities industry have trouble integrating new technologies for a long time, the benefits of quantum computing make it worth the trouble. In the past, it has been hard for the utilities industry to use new technology to drive strategic initiatives. But those who do see big improvements in how well they run their businesses. For example, utilities that put a lot of money into digital transformation have the chance to cut their operating costs by about 25%. Quantum computing is a new field of technology that is not as well developed as digital projects, but it is still something that modern renewable energy and utility companies should look into. Quantum computing will give utilities a lot more computing power, which will let them solve business problems that were too hard to try before.

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“…Aaryashree, Sahoo, et al [8] In this study, investigate the recent developments and potential benefits of smart chemical sensors that are both wearable and self-powered.…”

Section: Introductionmentioning

confidence: 99%

Utilization of Renewable energy for Industrial Applications using Quantum Computing

Rajawat

Pant

Goyal

2022

Glo.Jou.Nov.Res.App.Sci

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Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery

Tang,

Leung,

et al. 2024

ChemElectroChem

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This review exploits the crucial role of computational methods in discovering and optimizing materials for redox flow batteries (RFBs). Integration of high‐throughput computational screening (HTCS) and machine learning (ML) accelerates materials discovery, guided by algorithms categorizing RFBs. A collaborative exploration, spanning macroscopic to mesoscopic scales, combines quantum machine learning with reinforcement learning, transfer learning, time series analysis, Bayesian optimization, active learning and various generative models. The collaborative integration of ML with computational techniques and experimental methods, anchored in experimentally validated Density Functional Theory (DFT) calculations and molecular dynamics (MD) simulations, proves indispensable for cost‐effective RFBs. Data collection and feature engineering are explored, emphasizing the integration of optimization goals and precise data collection within the ML framework. Feature analysis importance is highlighted, utilizing methods such as the filter, embedded, wrapper and deep learning methods for efficient energy materials exploration. Computational perspectives on materials features and operating conditions encompass membrane characteristics, fluid dynamics, temperature dependence and pressure sensitivity. Time‐dependent features and ML‐generated insights are crucial for understanding cycling performance intricacies, providing a comprehensive understanding of RFB materials.

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Transparency: The Missing Link to Boosting AI Transformations in Chemical Engineering

Yuan,

Chaffart,

et al. 2024

Engineering

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Role of Computational Science in Materials and Systems Design for Sustainable Energy Applications: An Industry Perspective

Cited by 4 publications

References 123 publications

Utilization of Renewable energy for Industrial Applications using Quantum Computing

Utilization of Renewable energy for Industrial Applications using Quantum Computing

Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery

Transparency: The Missing Link to Boosting AI Transformations in Chemical Engineering

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